ArticleEntropy (Basel, Switzerland)2021
An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 72 citations in OpenAlex.
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- DRIME: A Distributed Data-Guided RIME Algorithm for Numerical Optimization Problems.Biomimetics (Basel, Switzerland) · 2025Article
- LSEWOA: An Enhanced Whale Optimization Algorithm with Multi-Strategy for Numerical and Engineering Design Optimization Problems.Sensors (Basel, Switzerland) · 2025Article
- Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO).Heliyon · 2024Article
- Optimization and inventory management under stochastic demand using metaheuristic algorithm.PloS one · 2024Article
- A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance.Scientific reports · 2023Article
- Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets.PloS one · 2023Article
- Multiple objective immune wolf colony algorithm for solving time-cost-quality trade-off problem.PloS one · 2023Article
- Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems.Sensors (Basel, Switzerland) · 2022Article
- A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm.Sensors (Basel, Switzerland) · 2022Article
- A twinning bare bones particle swarm optimization algorithm.PloS one · 2022Article
- Traffic flow forecasting using natural selection based hybrid Bald Eagle Search-Grey Wolf optimization algorithm.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 5 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO's issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.
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